How Digital Traceability Is Reshaping Food Safety? – II

Where Food Safety Management Systems Meet Digital Traceability

For food businesses operating under ISO 22000:2018 or FSSC 22000, the relationship between their existing food safety management system and emerging digital traceability requirements is not a new challenge. It is an extension of existing requirements that the standard already anticipates, even if implementation has historically lagged behind regulatory intent.
 
Clause 8.3 of ISO 22000:2018 establishes traceability as a fundamental system requirement, requiring the organization to ensure that the end products, work-in-process, and intermediate products can be identified by lot and linked to raw material batches, processing and packaging records, and distribution records. The standard requires that traceability records be maintained for a defined period sufficient to allow system evaluation and handling of potentially unsafe products, and be available to competent authorities and customers upon request. Critically, ISO 22000:2018 Clause 8.9 on handling of nonconformities and Clause 8.9.4 on withdrawals and recalls require the organization to be able to notify relevant interested parties and competent authorities in a timely manner, which is a requirement whose practical adequacy is directly determined by the speed and completeness of the traceability records supporting it.
 
What FSMA 204 and the corresponding EU regulatory framework are effectively doing is operationalizing and digitizing what ISO 22000 has required in principle since its first edition. The key data elements of FSMA 204: lot identification, location identifiers, dates of key events, quantities, and reference documents, map directly to the traceability record requirements of ISO 22000:2018. Businesses that have implemented ISO 22000:2018 rigorously and systematically are therefore not starting from zero; they are translating existing paper-based or partially digital record systems into the interoperable, machine-readable formats that regulators and major retail customers are increasingly requiring.

The competence requirements of Clause 7.2 and the awareness requirements of Clause 7.3 are also directly relevant: ensuring that the people responsible for data entry, system management, and supply chain communication have the skills to operate digital traceability systems, and understand why accurate data capture at each critical tracking event matters for food safety, is as important as the technology investment itself. A blockchain-backed traceability system populated by operators who do not understand what they are recording, or who take shortcuts under production pressure, delivers no meaningful improvement in food safety outcomes.
 
The ISO 22000 Connection, the Business Case and Beyond Compliance

The argument for digital traceability investment is sometimes framed exclusively as a compliance obligation — as though, absent regulatory requirements, there would be no business case for the capability. This framing is both strategically shortsighted and factually inaccurate.
 
The costs of a major recall without robust digital traceability are substantial and well-documented. The direct costs, such as product removal, destruction, logistics, retesting, and regulatory response, are significant. The indirect costs, such as brand damage, retailer delisting, litigation exposure, and long-term market share loss, are often larger. The Boar's Head outbreak resulted in the permanent closure of a production facility, extensive litigation, and reputational damage to a brand that had been established for over a century. McDonald's removed slivered onions from its menu nationally as a precaution while traceback investigations were ongoing — a decision that imposed supply chain disruption across a network of hundreds of thousands of restaurants [1].
 
A food business with robust digital traceability such as lot-level identification, real-time supply chain visibility, and the ability to query the full distribution of a specific lot within minutes can execute a targeted, surgical recall. Rather than recalling all product from a broad date range or all product from a facility, a targeted recall isolates the specific contaminated lot and removes only that product from commerce. The difference in scope, cost, and consumer impact between a targeted and a broad recall can be enormous. FDA research and industry experience consistently show that the speed of product removal is the single most important variable in limiting the number of illnesses in a foodborne outbreak, where speed of removal depends entirely on the quality and accessibility of traceability data.
 
Beyond recall management, the business case extends to supply chain efficiency, food waste reduction, and consumer trust. Digital traceability data, when properly structured and shared, enables better inventory management, more precise shelf-life optimization, and faster response to quality deviations before they become safety events. For retailers and food service operators, the ability to demonstrate verified provenance and supply chain transparency to consumers represents a growing source of competitive differentiation, particularly as consumer interest in food origin, sustainability, and safety continues to rise.
 
The Barriers: A Realistic Assessment
An honest treatment of digital traceability requires acknowledging the barriers that explain why adoption has been slower and more uneven than the technology's proponents might suggest.
 
Cost and Scalability for Small and Medium Enterprises

The cost of implementing FSMA 204-compliant systems such as ERP integration, barcode labelling infrastructure, EPCIS event capture, and EDI capability represents a significant investment for small and medium-sized food businesses. Industry feedback to the FDA has consistently cited cost as a primary barrier, and such concern is not abstract: a small leafy greens producer or a regional seafood processor may face implementation costs that represent a meaningful fraction of their annual revenue [9]. The 30-month extension to the FSMA 204 compliance deadline was granted in part because FDA acknowledged that smaller operators needed more time and, implicitly, more affordable implementation options.
 
This is where the FDA's Low/No-Cost Traceability Challenge, and the work of GS1 US in providing open standards and accessible resources, play a meaningful role. The FDA has also explicitly stated that the rule does not prescribe specific technologies, where a paper-based lot code on a bill of lading meets the technical definition of a Traceability Lot Code under the rule, even if it is far less efficient than a GS1-128 barcode [7]. The practical question for smaller operators is therefore not whether to comply, but how to do so in a way that is technically sufficient now and provides a foundation for more sophisticated digital capability over time.
 
Data Quality and System Integration
Traceability data is only as valuable as its accuracy. A traceability system that captures lot codes at the receiving dock but does not reliably link those codes through the transformation events on the production floor, or that has gaps in its data when product crosses from one software system to another, provides incomplete and potentially misleading information when a traceback investigation begins. Data quality problems are frequently a function of human factors such as inconsistent scanning practices, manual data entry errors, and lot code assignment gaps when incoming product lacks codes, rather than technology limitations per se [8].
 
Integrating traceability functions into existing ERP and production management systems, rather than running them as parallel processes, is the most effective way to reduce the data quality gap. When the system that generates a production order automatically populates the transformation CTE record, and the system that generates an outbound shipment automatically creates the shipping CTE record, the opportunities for human error are minimized and the data trail becomes a natural output of normal operations rather than an additional compliance burden.
 
Global Harmonization: A Work in Progress
As noted above, the multiplicity of regulatory frameworks such as FSMA 204 in the United States, EC Regulation 178/2002 and the EUDR in the EU, national frameworks in China, Japan, and elsewhere, creates compliance complexity for multinational food businesses and for exporters serving multiple markets. The lack of a single harmonized global traceability standard means that a company operating in multiple regulatory environments may need to maintain multiple data sets in multiple formats, potentially with different granularity requirements and different timelines for data provision to authorities.

Efforts toward harmonization are ongoing. The Global Food Safety Initiative (GFSI) and the International Featured Standards (IFS) both address traceability requirements in ways that are designed to be internationally recognizable, and GFSI's benchmarking work aims to ensure that food safety certification schemes recognized by major retailers reflect consistent traceability expectations [13]. However, the convergence of national regulatory requirements around a genuinely interoperable global standard remains a medium-term aspiration rather than a near-term reality.
 
What Food Safety Professionals and Businesses Must Do Now
The extension of FSMA 204's compliance deadline to July 2028 provides breathing room for technical implementation. But it does not change the strategic trajectory, and companies that treat it as an opportunity to defer planning rather than to improve their implementation are likely to find themselves in a difficult position as the deadline approaches.
 
Based on the current regulatory landscape, the industry intelligence available from IFT, FMI, FDA, and the peer-reviewed literature, and the practical experience of companies that are already implementing digital traceability systems, the following priorities merit immediate attention.
 
Map Your Traceability Data Gaps Now

The first step is a systematic internal assessment: for each product line subject to the Traceability Rule, trace the data chain from raw material receipt through processing, packaging, and outbound shipment. Identify where lot-level data is currently captured in a machine-readable format, where it is captured on paper, and where it is not captured at all. Such gap analysis provides the foundation for an implementation plan that prioritizes the highest-risk data gaps and the steps that are prerequisite to others.
 
Align on GS1 Standards Across the Supply Chain
Companies that have not yet adopted GS1 standards for product and location identification should treat this as a foundational priority. GS1 GTINs and GLNs provide the unique identifiers that allow KDE data to be exchanged between different supply chain actors and different software systems — they are the common language without which interoperability is impossible [12]. Engaging suppliers and distribution partners in conversations about GS1 adoption is not merely a courtesy; it is a supply chain risk management necessity.
 
Invest in System Integration, Not Parallel Processes
The most common implementation failure in traceability projects is the creation of a parallel traceability system that operates alongside existing ERP and production management systems rather than being integrated into them. Data that must be manually entered twice — once into the production system and once into the traceability system — will not be entered accurately and consistently. The implementation goal should be a single workflow in which normal operational transactions automatically generate the required traceability records.
 
Engage the Supply Chain Upstream and Downstream
The FDA's own stakeholder engagement has repeatedly confirmed that the most significant traceability gaps tend to be at the boundaries between supply chain actors, particularly at the farm and first-receiver level where digital systems are least mature [9]. Food manufacturers who have invested in internal traceability systems but whose produce suppliers are still using paper manifests need to actively support their suppliers' transition to digital traceability, whether through technical assistance, financial support, or trading partner agreements that set data quality expectations.
 
Build ISO 22000 Traceability Competence
Organizations certified to ISO 22000:2018 should ensure that their competence and awareness programs (Clauses 7.2 and 7.3) explicitly address digital traceability. This means not just ensuring that people know how to operate the relevant systems, but ensuring that they understand why accurate and complete data capture at each critical tracking event is a food safety function, and not merely an administrative one. The food safety management system documentation should explicitly connect the traceability requirements of Clause 8.3 to the specific KDEs and CTEs required under FSMA 204 or the applicable national regulatory framework.
 
Looking Ahead: The Direction of Travel

The regulatory and technological direction is unambiguous. A 2025 Frontiers review of European agri-food digitalization concluded that the further deployment of IoT, RFID, and QR code technologies, combined with investment in harmonized standards, shared APIs, and common data taxonomies, will progressively reduce the cost and complexity barriers that currently slow adoption [14]. The FDA's active engagement with stakeholders through mid-2026, including its June 2026 public meeting on lot-level tracking, signals that the agency is working to make the final requirements as practically implementable as possible, without stepping back from the fundamental commitment to digital, lot-level, interoperable traceability [9].
 
IFT's Global Food Traceability Center, led by Managing Director Blake Harris, has been a consistent source of practical guidance and educational resources throughout the FSMA 204 implementation cycle, and its role in helping organizations navigate the transition from compliance planning to operational capability is likely to grow [3]. The academic literature, particularly the rapidly expanding body of peer-reviewed research on blockchain, IoT, and AI applications in food supply chains, is generating an increasingly evidence-based understanding of what works, what fails, and why — providing food safety professionals with more reliable guidance than was available at any prior point in the traceability technology's development.
 
The analogy that is perhaps most instructive is HACCP. When the HACCP system was first mandated for meat and poultry in the United States in 1996, and for seafood shortly after, the food industry faced a transition of comparable scope and complexity: a systematic, science-based approach to hazard identification and control that required new documentation, new competencies, and new thinking about the relationship between food safety and production operations. The industry built that capability over time, and HACCP is now so deeply embedded in food safety practice that it is almost invisible — it is simply how food safety is done. Digital traceability is following the same trajectory. The companies that invest in building the capability now, before the compliance deadline creates a scramble, will not merely be compliant — they will be competent. And in food safety, competence is the only standard that matters.
 
Conclusion

Digital traceability is not a trend. It is a transition, from a food safety architecture built on reactive traceback to one built on proactive data infrastructure. The human cost of the gap between where the industry is and where it needs to be was visible in 2024, in the delayed identification of contaminated deli meat and the drawn-out investigation of an outbreak that reached 14 states before the contaminated onion supply was removed. It is visible in the 44 percent of outbreak investigations that still cannot identify a food vehicle of illness. And it will remain visible for as long as the industry's traceability systems depend on paper, incompatible formats, and the patience of epidemiologists rather than on machine-readable data that can be queried in minutes.
 
The regulatory framework is now moving, whereas in the United States, the European Union, and globally — to make digital traceability mandatory, not aspirational. The technologies to implement it exist and are improving. The standards infrastructure, led by GS1, is in place. What remains is the organizational commitment to translate regulatory obligation into operational capability, supply chain collaboration to close data gaps at the boundaries between actors, and investment in the people and systems that make the data reliable.
 
For food safety professionals, the question is not whether digital traceability is coming. It is already here, in the requirements of major retailers, in the FDA's New Era of Smarter Food Safety Blueprint, in the FSMA 204 rule whose compliance date is firm at July 2028, and in the expectations of consumers who are paying closer attention to what they eat than at any prior point in history. The question is whether your organization is building the capability now, deliberately and systematically, or waiting for the deadline to force a scramble that will cost more and deliver less.
 
The data that traces food through the supply chain is, ultimately, the data that protects the people who eat it. It is time to take it seriously.
 
 

References

[1] U.S. Public Interest Research Group (PIRG) Education Fund. (February 2025). Food for Thought 2025: How safe is our food?. https://pirg.org/edfund/resources/food-for-thought-2025/

[2] Prabhukhot, G. (2026). Regulatory responses to foodborne illness outbreaks in the United States and their implications for food safety. Frontiers in Nutrition, 12, 1717980. https://doi.org/10.3389/fnut.2025.1717980

[3] Niemira, B. (December 2025). What's on the Menu for 2026? IFT's Top Five Food Trends. Institute of Food Technologists. https://www.ift.org/news-and-publications/blog/2025/whats-on-the-menu-for-2026

[4] Eisenbeiser, A. (February 9, 2026). Building the Safest Food System Together: FMI's 2026 Food Safety Priorities. FMI – The Food Industry Association. https://www.fmi.org/blog/view/fmi-blog/2026/02/09/building-the-safest-food-system-together--fmi-s-2026-food-safety-priorities

[5] Reitano, A., et al. (2025). Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends in Food Science & Technology. https://www.sciencedirect.com/science/article/pii/S0924224425002900

[6] Frontiers in Sustainable Food Systems. (April 2026). Food safety and its digital traceability strategies: a supplier-processor profit distribution perspective. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1707114/full

[7] U.S. Food and Drug Administration. FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods (Food Traceability Final Rule). https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods

[8] INECTA. (2026). FSMA 204 Compliance Guide: KDEs, CTEs & July 2028 Deadline. https://www.inecta.com/blog/fsma-204-compliance-guide

[9] OFW Law. (June 23, 2026). FDA's Next Steps on Traceability: Challenges and Solutions in Lot-Level Food Traceability. https://ofwlaw.com/fdas-next-steps-on-traceability-challenges-and-solutions-in-lot-level-food-traceability

[10] Gottschald, M. (2024). Advancing food safety through digital traceability, interoperability, harmonized data and collaborative partnerships. Journal of Consumer Protection and Food Safety, 19, 257–258. https://doi.org/10.1007/s00003-024-01522-8

[11] Natural Trace. (2024). Recent Regulations Driving Traceability in Food and Agriculture Sectors. https://natural-trace.com/recent-regulations-driving-traceability-in-food-and-agriculture-sectors/

[12] GS1 US. Food Safety Modernization Act (FSMA 204): How GS1 Standards Can Help. https://www.supplychain.gs1us.org/standards-and-regulations/food-safety-modernization-act

[13] Vasileiou, K., et al. (2025). Digital Transformation of Food Supply Chain Management Using Blockchain: A Systematic Literature Review Towards Food Safety and Traceability. Business & Information Systems Engineering. https://doi.org/10.1007/s12599-025-00948-0

[14] Frontiers in Blockchain. (October 2025). Digitalization in the European agri-food supply chain: a scoping review of traceability, transparency, and sustainability. https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2025.1701872/full

 

How Digital Traceability Is Reshaping Food Safety?

 Why Companies Must Adapt Now

The year is 2024, a multistate Listeria outbreak linked to Boar's Head deli meats has sickened 61 people across 19 states, hospitalised 60, and killed 10. The first patient was identified on May 29. The recall was not initiated until July 26, nearly two months later. In that interval, people continued consuming contaminated products, right up until the plant was closed in September. The same year, onions supplied to McDonald's Quarter Pounder hamburgers triggered an E. coli O157:H7 outbreak spanning 14 states, causing more than 100 illnesses, four cases of hemolytic uremic syndrome, and one death. According to data from the U.S. Public Interest Research Group, hospitalizations from foodborne illness in 2024 more than doubled compared to the previous year — from 230 to 487 — and deaths rose from 8 to 19 [1].

 

Neither of these outbreaks was, at its core, a mystery. What they were was slow, where slow to identify the contaminated lot and slow to trace it back through a supply chain that still relies, in significant parts, on paper-based records, incompatible software systems, and manual data entry. Slow to remove product from the market with the precision that modern technology should allow. The CDC's CORE Network data shows that, across 2020–2025, a food vehicle of illness was identified for only 56 percent of outbreak investigations, meaning 44 percent remained unsolved [2]. This is not a new statistic. It is a stubborn one. And it is precisely the number that the global push toward digital traceability is designed to change.

 

The transition is now happening at regulatory speed. The Institute of Food Technologists (IFT) has identified digital tools expanding food safety adoption as one of its top five trends shaping the global food system in 2026 [3]. The Food Marketing Institute (FMI), representing the retail food industry, has named traceability the number one food safety priority for 2026, placing it ahead of produce safety, chemical safety, and sanitation controls [4]. At the regulatory level, the U.S. FDA's Food Safety Modernization Act Rule 204, which was one of the most consequential pieces of food safety regulation in a generation, is rewriting the technical requirements for supply chain data across the entire food industry. Further beyond the United States, the European Union, China, and major trading blocs are simultaneously developing their own digital traceability frameworks, raising fundamental questions about interoperability, harmonization, and what it truly means to build a globally connected, digitally transparent food system.

 

The article examines what digital traceability is, why the industry is being compelled to adopt it now, what the regulatory landscape looks like globally, what technologies are enabling it, what barriers remain, and what food safety professionals and businesses must do in the near term to position themselves on the right side of a transition that is no longer optional.

 

What is Digital Traceability

Before examining the regulatory and technological landscape, it is worth being precise about the term itself, because "traceability" is used loosely in industry contexts in ways that can obscure meaningful distinctions.

 

Traceability, in its regulatory and scientific sense, is the ability to identify and follow the movement of a food product — or a substance intended to be incorporated into a food or feed — through all stages of production, processing, and distribution. The European Union's foundational General Food Law, EC Regulation 178/2002, established it as a legal requirement for all food and feed operators operating in the EU, using what it describes as a "one-step-back, one-step-forward" principle: each operator must be able to identify from whom they received a product and to whom they supplied it [5].

 

What makes digital traceability different from traditional traceability is the nature of the data and the speed at which it can be exchanged, queried, and acted upon. Traditional traceability systems relied on paper-based records, spreadsheets, and proprietary software that managed internal business processes without enabling real-time data exchange between supply chain actors. Digital traceability, by contrast, uses a combination of technologies — barcodes, RFID tags, QR codes, IoT sensors, electronic product code information services (EPCIS), blockchain, and cloud-based platforms — to create a continuous, machine-readable data trail that can be queried by any authorized party in real time [6].

 

The significance of the distinction becomes apparent the moment a recall is needed. A food business that can identify the contaminated lot within minutes and trace every downstream recipient within an hour is operating in a qualitatively different risk environment from one that requires days of manual record-searching. The FDA's vision, articulated in its New Era of Smarter Food Safety Blueprint, is explicit: the goal is faster and more targeted recalls, reduced scope of product removal, fewer illnesses, and ultimately lower costs for both industry and the public [7].

 

The Regulatory Architecture: What Is Being Required, and When

FSMA Rule 204: The United States

The FDA's Food Traceability Final Rule, commonly known as FSMA 204, was published in November 2022 and represents the most substantive expansion of federal traceability requirements since FSMA itself was enacted in 2011. At its core, the rule requires all persons who manufacture, process, pack, or hold foods included on the Food Traceability List (FTL) to maintain records containing Key Data Elements (KDEs) associated with Critical Tracking Events (CTEs), and to be able to provide those records to the FDA within 24 hours upon request [7].

 

The FTL covers a broad range of high-risk commodities: soft and semi-soft cheeses, shell eggs, nut butters, leafy greens, fresh herbs, cucumbers, peppers, tomatoes, sprouts, melons, tropical tree fruits, fresh-cut produce, certain finfish and molluscan shellfish, smoked finfish, crustaceans, and refrigerated ready-to-eat salads [7]. The scope is deliberately wide — these are the categories most frequently implicated in large, multi-state outbreaks, and they represent a substantial share of produce and protein consumption across the United States and for the global exporters who supply the U.S. market.

 

The rule's original compliance deadline of January 20, 2026, has been extended by 30 months to July 20, 2028, following an FDA announcement in March 2025 that acknowledged both the complexity of the rule and the significant technical preparation required across a diverse supply chain [8]. The 30-month extension was subsequently codified by Congress in the Continuing Appropriations Act of 2026. The FDA has been clear that this extension is a window for technical preparation, not a signal that the requirements are being relaxed. The agency hosted a major public stakeholder meeting on lot-level tracking as recently as June 15, 2026, and has actively solicited input on implementation flexibilities while holding firm on the fundamental requirement for digital, lot-level data capture and exchange [9].

 

One consequential development that underscores how the industry is not waiting for federal compliance dates: Walmart's supplier traceability requirements, mandating Advance Shipment Notices with KDE data, SSCC-18 pallet labels, and GS1-128 case labels, which took effect in August 2025, with chargebacks for non-compliant shipments already being assessed [8]. For the large proportion of food manufacturers serving mass retail, the federal compliance date is no longer the operational driver. Their largest customer's requirements already are.

 

The European Union Framework

The EU's approach to food traceability is embedded in a layered legislative architecture, where EC Regulation 178/2002 provides the foundational requirement for traceability across all food and feed operators [5]. On top of the given general framework sits sector-specific regulations for beef and beef labelling, fish and aquaculture products, genetically modified organisms, and organic produce. The EU Food Safety Authority (EFSA) and the German Federal Institute for Risk Assessment (BfR) have been actively developing a Universal Traceability data eXchange (UTX) format and an interoperable multi-actor tracing software ecosystem to support outbreak investigation and rapid alert systems [10].

The EU's Rapid Alert System for Food and Feed (RASFF) is one of the most mature food safety alert networks in the world, and the push toward digital traceability is in part designed to allow RASFF to function at the speed that modern supply chains demand. A 2024 editorial in the Journal of Consumer Protection and Food Safety makes the point directly: food safety authorities worldwide must intensify their efforts to collect and utilize digital traceability data, because as supply chains advance toward Industry 4.0, incorporating IoT sensors and digital twins, the volume of data will grow exponentially and authorities must keep pace [10].

 

The EU Deforestation Regulation (EUDR), which entered into force in 2023 and applies to a range of food commodities including soy, beef, palm oil, and cocoa, adds a further dimension to the EU's traceability requirements: companies must demonstrate that their products are free from deforestation, which in practice requires geolocation data and supply chain documentation down to the production plot level [11]. This represents a significant escalation in what traceability means in the EU context, which is not merely lot-level identification for recall purposes, but verifiable origin data at the level of individual farms and geographic coordinates.

 

China and the Broader Global Context

China has been actively developing national food traceability systems to address domestic food safety concerns and to support export competitiveness. The Chinese government has implemented a series of traceability platforms, including systems specific to pork, dairy, and infant formula, following high-profile food safety scandals that severely damaged consumer trust in domestic producers. A 2025 review in ScienceDirect notes that China's national food traceability architecture is evolving rapidly, though integration across regional systems and harmonization with international data standards remain works in progress [5].

 

The global picture, then, is one of multiple regulatory frameworks converging on a shared direction, “mandatory digital traceability”, while diverging in their specific technical requirements, covered commodities, and timelines. Thus, given divergence has significant practical implications for exporters operating across multiple regulatory environments. A company exporting leafy greens to the United States, fresh fish to the European Union, and dairy products to China must navigate three distinct traceability frameworks simultaneously, and the data formats, identification standards, and information disclosure requirements may differ materially between them.

 

The Technology Layer: What is Enabling Digital Traceability

The technologies underpinning digital traceability form an integrated ecosystem rather than a collection of isolated tools. Understanding how they work together is essential for food businesses making investment and implementation decisions.

 

GS1 Standards: The Universal Language

GS1 is the international, not-for-profit standards organization responsible for the global identification and communication standards that underpin product traceability. Its standards — particularly the Global Trade Item Number (GTIN) for product identification, the Global Location Number (GLN) for location identification, and the SSCC-18 for serialized shipping unit identification — are explicitly recognized by the FDA as a mechanism for meeting the KDE requirements of FSMA 204 [12]. The GS1-128 barcode encodes the GTIN, lot code, expiry date, and quantity on each case, and when combined with GS1's EPCIS (Electronic Product Code Information Services) standard for event data sharing, creates a foundation for interoperable, multi-actor traceability that does not require all parties in a supply chain to use the same software platform [12].

 

The practical significance of GS1 standards is that they represent the closest thing the industry currently has to a universal language for traceability data. A farm that captures harvest data using GS1-compliant identifiers, a processor that records transformation events using EPCIS, a distributor that generates GS1-128 case labels, and a retailer that scans those labels can all exchange traceability information without custom integrations — provided they are using standards-compliant systems. The "provided" clause is doing significant work in that sentence, because adoption of GS1 standards across the full supply chain is still uneven, particularly among smaller operators and producers in low- and middle-income countries [9].

 

Blockchain: Immutability and Multi-Party Trust

Blockchain technology in food traceability has attracted considerable attention since Walmart's landmark 2018 partnership with IBM Food Trust demonstrated that the time required to trace a food item from store to farm could be reduced from approximately seven days to 2.2 seconds using a blockchain-based system. By 2025, a systematic literature review in Business & Information Systems Engineering found that blockchain was the most frequently studied technology for food traceability, appearing in more than 40 percent of selected studies, typically deployed in combination with IoT sensors, RFID tags, or QR codes [13].

 

The fundamental contribution of blockchain to traceability is immutability — once data is entered into a distributed ledger, it cannot be altered retroactively without detection. Such property is valuable in the context of food fraud and in supply chains where multiple parties need to trust each other's records without placing complete confidence in any single actor's database. A 2024 research implementation reported in the blockchain literature showed fraud incident reductions of 80 percent and a rise in fraud detection rates from 70 to 95 percent, with consumer satisfaction index scores rising 12.5 percent [13].

 

However, blockchain's limitations are as important to understand as its benefits. The technology cannot protect against fraud that occurs before data is entered into the system, and the integrity of the physical-digital link depends entirely on the accuracy of the labelling and scanning processes at the point of data capture. As a Frontiers review noted, blockchain integration also requires the combination with IoT sensors and smart tags that automatically collect data, reducing the risk of human error or falsification; without this combination, the immutability of the ledger is only as strong as the honesty of the person entering the data [5]. Cost and scalability remain significant barriers, particularly for smaller operators.

 

IoT and Real-Time Monitoring

Internet of Things sensors: temperature loggers, GPS trackers, RFID readers, and humidity monitors, provide the data capture layer that converts physical events in the supply chain into machine-readable records. Under FSMA 204, the FDA's concept of Critical Tracking Events includes not just growing, receiving, transforming, and shipping, but the conditions under which food is held and transported. IoT sensors can automatically log these conditions in real time, generating continuous data streams that can populate KDE records without manual data entry and trigger alerts when conditions deviate from safe parameters.

 

The FDA's own Low/No-Cost Traceability Challenge, a program designed to identify accessible traceability solutions for smaller operators, recognized both blockchain and IoT as breakthrough solutions precisely because of such automation potential [12]. The cost of IoT sensor hardware has fallen significantly over the past decade, and cloud-based data platforms that aggregate sensor data from multiple supply chain actors are increasingly accessible. For cold chain management specifically, a critical dimension of traceability for fresh produce, seafood, and dairy, where IoT monitoring is rapidly transitioning from a value-added feature to a baseline expectation among major retailers and regulatory bodies.

 

Rapid and Digital Testing Integration

A dimension of digital traceability that is sometimes overlooked is its integration with rapid testing at production and processing points. Next-generation sequencing, rapid immunoassay platforms, and digital PCR systems are generating pathogen detection data in hours rather than days, and the ability to link that testing data directly to lot-level traceability records creates a closed loop between quality control and supply chain documentation. Whole genome sequencing (WGS), already used by FDA and CDC in outbreak investigations to match environmental strains to clinical isolates, is being positioned as the epidemiological backbone of the next generation of foodborne illness surveillance, but its value is amplified when the supply chain records it needs to cross-reference are digital, lot-level, and rapidly accessible [2].

 

The Interoperability Problem: Why Standards Alone Are Not Enough

The single most consequential structural challenge facing digital traceability implementation is interoperability, the ability of different software systems used by different actors in the supply chain to exchange and analyze data accurately and efficiently. The point at which good intentions most frequently collide with operational reality.

 

A 2024 editorial in the Journal of Consumer Protection and Food Safety, authored by Marion Gottschald of the German Federal Institute for Risk Assessment, made this structural problem explicit: while the food industry uses numerous tracing software systems, they are mostly focused on managing internal business processes rather than facilitating data exchange between actors [10]. Food safety authorities have access to some inter-agency tools, including RASFF and FoodChain-Lab, but the widespread adoption of genuinely interoperable software is limited by the lack of available digital traceability data and the limited standardization of tracing data formats across the industry.

 

A 2025 Frontiers systematic review of digitalization in European agri-food supply chains echoed that finding: most studies in the peer-reviewed literature describe conceptual frameworks or pilot implementations rather than fully realized systems, and real-world deployment is hampered by interoperability challenges, scalability issues, regulatory uncertainties, and high costs [14]. The review found that to ensure interoperability across processing and retail stages, harmonized standards, shared APIs, and common data taxonomies are needed, which is a recommendation that has been made many times and implemented unevenly.

 

The practical implication for a food business today is that investing in a traceability system that works internally but cannot communicate with the systems used by suppliers and customers upstream and downstream does not fully deliver on the promise of digital traceability. The value of traceability data is a network effect, which increases with the number of actors who can access, contribute to, and act on it. A manufacturer who has invested in GS1-compliant systems and EPCIS event sharing but whose primary fresh produce supplier is still using paper manifests is operating with a significant gap in their data chain.

 

The FDA stakeholder meeting of June 2026 on lot-level tracking heard exactly the concern from food industry representatives: traceability data today comes in many formats (paper, spreadsheets, and incompatible software systems), which creates both inefficiencies and compliance risks. Participants broadly identified GS1 standards as the most practical common language for global supply chains, while acknowledging that adoption is uneven and that the costs and technical barriers for smaller and less capitalized operators are real [9].

 

TO BE CONTINUED

 

References

[1] U.S. Public Interest Research Group (PIRG) Education Fund. (February 2025). Food for Thought 2025: How safe is our food?. https://pirg.org/edfund/resources/food-for-thought-2025/

[2] Prabhukhot, G. (2026). Regulatory responses to foodborne illness outbreaks in the United States and their implications for food safety. Frontiers in Nutrition, 12, 1717980. https://doi.org/10.3389/fnut.2025.1717980

[3] Niemira, B. (December 2025). What's on the Menu for 2026? IFT's Top Five Food Trends. Institute of Food Technologists. https://www.ift.org/news-and-publications/blog/2025/whats-on-the-menu-for-2026

[4] Eisenbeiser, A. (February 9, 2026). Building the Safest Food System Together: FMI's 2026 Food Safety Priorities. FMI – The Food Industry Association. https://www.fmi.org/blog/view/fmi-blog/2026/02/09/building-the-safest-food-system-together--fmi-s-2026-food-safety-priorities

[5] Reitano, A., et al. (2025). Agri-food traceability today: Advancing innovation towards efficiency, sustainability, ethical sourcing, and safety in food supply chains. Trends in Food Science & Technology. https://www.sciencedirect.com/science/article/pii/S0924224425002900

[6] Frontiers in Sustainable Food Systems. (April 2026). Food safety and its digital traceability strategies: a supplier-processor profit distribution perspective. https://www.frontiersin.org/journals/sustainable-food-systems/articles/10.3389/fsufs.2025.1707114/full

[7] U.S. Food and Drug Administration. FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods (Food Traceability Final Rule). https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods

[8] INECTA. (2026). FSMA 204 Compliance Guide: KDEs, CTEs & July 2028 Deadline. https://www.inecta.com/blog/fsma-204-compliance-guide

[9] OFW Law. (June 23, 2026). FDA's Next Steps on Traceability: Challenges and Solutions in Lot-Level Food Traceability. https://ofwlaw.com/fdas-next-steps-on-traceability-challenges-and-solutions-in-lot-level-food-traceability

[10] Gottschald, M. (2024). Advancing food safety through digital traceability, interoperability, harmonized data and collaborative partnerships. Journal of Consumer Protection and Food Safety, 19, 257–258. https://doi.org/10.1007/s00003-024-01522-8

[11] Natural Trace. (2024). Recent Regulations Driving Traceability in Food and Agriculture Sectors. https://natural-trace.com/recent-regulations-driving-traceability-in-food-and-agriculture-sectors/

[12] GS1 US. Food Safety Modernization Act (FSMA 204): How GS1 Standards Can Help. https://www.supplychain.gs1us.org/standards-and-regulations/food-safety-modernization-act

[13] Vasileiou, K., et al. (2025). Digital Transformation of Food Supply Chain Management Using Blockchain: A Systematic Literature Review Towards Food Safety and Traceability. Business & Information Systems Engineering. https://doi.org/10.1007/s12599-025-00948-0

[14] Frontiers in Blockchain. (October 2025). Digitalization in the European agri-food supply chain: a scoping review of traceability, transparency, and sustainability. https://www.frontiersin.org/journals/blockchain/articles/10.3389/fbloc.2025.1701872/full

AI and Food Safety: Can We Trust Machine-Generated Food Advice?

AI vs. Food Safety

The year is 2026, and a home cook in Auckland asks a voice assistant whether leftover rice left at room temperature for eight hours is safe to reheat and eat. The assistant confidently says yes. In Singapore, a food safety manager queries a generative AI chatbot for the regulatory allergen labelling thresholds for sesame in packaged foods, and the model returns a figure that is almost correct, off by a decimal point, and referencing a regulatory version that was superseded two years ago. In both cases, the AI responded fluently, confidently, and incorrectly. Nobody died, but the pattern is concerning, and in food safety, patterns like this eventually produce outcomes that matter very much. Thus, artificial intelligence is reshaping food safety communication, but also introducing new and poorly understood risks.

 

This is not a theoretical argument against artificial intelligence. AI is already delivering real and verifiable benefits across the food safety domain — from pathogen detection in laboratory settings to predictive import surveillance at border controls. The Food and Agriculture Organization of the United Nations (FAO), in its landmark 2025 technical publication developed jointly with Wageningen Food Safety Research, reviewed 141 scientific papers and documented practical AI deployments across inspection, surveillance, border control prioritisation, regulatory efficiency, and risk communication[1]. The report positions AI as a present-day tool, not a future aspiration. That is important context. AI in food safety is not hype — it is happening, and in many cases, it is working.

 

The Communication Gap

Traditional food safety communication had a relatively clear architecture. Regulatory bodies published guidelines. Industry implemented them. Accredited laboratories verified. Certified professionals interpreted the results. Consumers received simplified messages through labelling, public health campaigns, and their general practitioners. The chain was imperfect, but accountability was traceable, which is the communication gap nobody planned for. When something went wrong, there was usually a responsible party identifiable within the system.

 

Generative AI and voice assistants are disrupting this architecture in ways the food safety community has not yet fully reckoned with. Increasingly, both consumers and food industry professionals are bypassing traditional information channels and asking AI systems for food safety guidance directly. This is not a marginal behaviour. According to data from multiple technology research sources, AI-generated search summaries now appear at the top of results for a significant proportion of food-related queries in major markets, and voice assistants handle tens of millions of food-related questions per day globally.

The International Association for Food Protection (IAFP) 2025 Annual Meeting dedicated a full symposium to cutting through the hype of AI in food safety, and the concerns raised by speakers from Chick-fil-A, Ecolab, and the FDA were instructive[2]. David Monk of Chick-fil-A explicitly warned about hallucinations in large language models, the phenomenon where AI models generate plausible but factually fabricated information, and stressed the irreplaceable need for human oversight. Amani Babekir of Ecolab reinforced this directly: AI, she noted, will not eliminate the need for subject matter experts[2]. These are practitioners speaking from real deployment experience, not theoretical concerns.

 

The 2025 FAO report made the same point with institutional weight, explicitly identifying AI hallucinations, where models generate plausible but fabricated information, as a core risk, and warning that premature use of AI in food safety, whether by applying unsuitable techniques or implementing AI without the expertise to interpret its outputs, risks undermining the trust and credibility of the organisations employing it[1].

 

Root Causes

To address the problem properly, it is necessary to understand why it exists rather than simply cataloguing its symptoms. The root causes are structural, and they operate at multiple levels simultaneously.

 

Training Data Quality and Recency

Large language models and generative AI systems are trained on datasets assembled from the internet, scientific publications, regulatory documents, and other text sources. Food safety regulation is a domain characterised by frequent revision. Codex Alimentarius updates maximum residue limits. National regulatory bodies revise allergen thresholds. Recall databases are updated in real time. An AI model trained on data from eighteen months ago may confidently provide guidance based on superseded standards, and the model itself has no awareness of such a limitation, where it does not know what it does not know. The International AI Safety Report 2026 noted explicitly that AI systems can generate non-existent citations, biographies, or facts due to the hallucination phenomenon, with confidence indistinguishable from accurate information[3].

 

Confidence Calibration and the Absence of Uncertainty Signals

Human food safety experts communicate uncertainty. A microbiologist asked about the safety of a novel fermentation process, will hedge, qualify, and direct the questioner to primary sources. Generative AI systems are optimised, in many deployment contexts, to produce fluent and complete-sounding responses. The very qualities that make them engaging as interfaces, conversational fluency, apparent confidence, and absence of hesitation, are precisely the qualities that make them dangerous in high-stakes informational contexts. A consumer asking whether their food is safe to eat needs not just an answer, but an appropriately calibrated signal about how certain that answer is, where current consumer-facing AI systems are structurally poor at delivering it.

 

Regulatory Fragmentation and Jurisdictional Ambiguity

Food safety regulation is deeply jurisdictional, where the maximum level for aflatoxin B1 in cereals intended for direct human consumption is 2 μg/kg in the European Union and 20 μg/kg in the United States, for example, thus a tenfold difference that reflects different risk assessment methodologies and policy choices, not a factual disagreement about toxicology. An AI system that does not know the user's jurisdiction, or that defaults to one regulatory context when the user is operating in another, can deliver technically accurate information for the wrong regulatory environment. In a world where food businesses increasingly operate across multiple jurisdictions, and consumers travel internationally, this is not a minor edge case.

 

Biased and Unrepresentative Training Corpora

A further structural problem is that the training data for general-purpose AI models is heavily skewed toward high-resource, English-language, Western regulatory contexts. Food safety guidance for ASEAN markets, African regulatory frameworks, or small island developing states is systematically underrepresented. A food safety manager in Indonesia, Ghana, or Samoa who queries an AI system in English is likely to receive responses calibrated to FDA or EFSA standards, which may be entirely inapplicable to their regulatory environment and local food production context. The 2025 FAO report noted that data gaps are particularly pronounced for low- and middle-income countries[1], and such gaps translate directly into AI advice that is geographically and contextually unreliable.

 

Accountability Gaps in the Information Chain

Traditional food safety communication is embedded in accountability structures. A food safety consultant who provides incorrect advice can face professional and legal consequences. A regulatory body that publishes incorrect guidance is accountable to its mandate and subject to legislative oversight. An AI system that provides incorrect food safety advice sits outside virtually all of these accountability frameworks, because there is no licensing body for AI food safety advisors, and there is no professional indemnity requirement either. Hence, there is no systematic post-market surveillance of AI-generated food safety information analogous to the adverse event reporting systems that govern medical devices and pharmaceuticals, where such an accountability gap is not a minor regulatory oversight, which is a structural vulnerability that the food safety governance community has barely begun to address.

 

Real Power of AI

A critical analysis must acknowledge genuine achievement alongside genuine risk, and AI in food safety has genuine achievements worth examining carefully, because they also illuminate where the risks concentrate.

 

The FDA has deployed a boosted-tree machine learning model, which is specifically LightGBM, to predict the probability that an imported food shipment will violate regulatory requirements. By combining data on shipment history, product characteristics, and exporting establishment and country risk indicators, the model improves targeting efficiency and increases the likelihood of intercepting unsafe products at the border. This is a well-designed application of AI: it operates within a domain where the training data is well-defined, the outcome is measurable, the model's predictions are reviewed by human inspectors before action is taken, and the consequences of error are caught by subsequent verification steps rather than transmitted directly to end users.

 

Similarly, AI-enabled computer vision systems in food manufacturing environments, by detecting contaminants, verifying packaging integrity, and monitoring temperature compliance in real time, represent applications where AI augments human inspection capacity in controlled, verifiable, high-frequency tasks. The model's outputs are checked against physical reality continuously, errors are corrected in the production flow, and the system operates under the supervision of qualified food safety professionals.

 

In food safety more broadly, AI enables predictive risk modelling, rapid contaminant detection, smart surveillance systems, and blockchain-based traceability, all within contexts where expert human oversight is embedded in the workflow. The pattern that distinguishes good AI deployment from risky AI deployment in food safety is clear: human expertise in the loop, measurable and verifiable outcomes, appropriate uncertainty communication, and domain-specific training data of known quality.

 

The risk concentrates precisely where these conditions are absent, and consumer-facing AI communication, where the advice goes directly to an end user without expert intermediation, is the domain where most of these conditions are missing.

 

Mitigation Strategies

The answer to the question "Can we trust machine-generated food advice?" is not a binary yes or no, which is a conditional answer that depends on context, deployment design, governance, and user literacy. The following mitigation strategies reflect the current state of knowledge and are graded by feasibility and urgency.

 

Domain-Specific AI Systems with Curated, Versioned Knowledge Bases

The most direct technical mitigation is to develop food safety AI applications that do not rely on general-purpose large language models trained on undifferentiated internet data, but instead use curated, jurisdictionally specific, version-controlled knowledge bases. Regulatory databases, Codex Alimentarius texts, and national food standards can be structured as retrieval-augmented generation (RAG) systems, where the AI's outputs are anchored to specific, dated regulatory documents rather than statistical generalisations from training data. This architecture allows the system to say "this answer is based on EU Regulation 2023/XXX, which was current as of this date" rather than generating a confident response from poorly attributed training data. Such systems are technically feasible now, and several national food safety authorities are beginning to pilot them.

 

Mandatory Uncertainty Communication and Source Attribution

AI systems deployed in food safety communication contexts should be required to communicate uncertainty explicitly and to attribute their responses to specific sources. Thus, the choice is not merely a technical design choice, but it should be a regulatory requirement for any AI system that provides food safety guidance in a commercial or public health context. The analogy is nutritional labelling, where it must be such that the food manufacturers are required to declare what is in their product; AI food safety systems should be required to declare the basis and confidence level of their recommendations. The 2026 International AI Safety Report's documentation of AI hallucination risks provides the public health rationale for making this a regulatory rather than voluntary standard[3].

 

Regulatory Frameworks for AI-Generated Food Safety Information

There is currently no coherent international regulatory framework governing AI-generated food safety advice, which is a major gap that Codex Alimentarius, the FAO, and national food safety authorities need to address with some urgency. The framework does not need to be restrictive, but it needs to be clear. At minimum, it should establish that AI systems providing food safety guidance must meet defined accuracy standards, disclose their training data provenance and recency, provide source attribution, communicate uncertainty, and be subject to post-market surveillance for accuracy. The EU AI Act's risk-based classification framework provides one possible model, though its application to food safety communication specifically remains underdeveloped. The International AI Safety Report 2026 notes the importance of expert human oversight as a mitigation for AI hallucination risks[3], and regulatory frameworks should embed this requirement structurally.

 

Organisational AI Literacy in Food Businesses

AI has the potential to improve food safety training and communication, but communication can be hindered by various forms of noise, including channel limitations, time pressure, and message complexity. Food businesses that are deploying AI tools for internal food safety management, whether for HACCP documentation, supplier audit management, or staff training, need to invest in AI literacy as a food safety competency. Thus, training food safety teams to understand what AI systems can and cannot reliably do, to verify AI-generated regulatory information against primary sources, and to recognise the signs of hallucinated or outdated content is an upcoming requirement as it will safeguard the industry while expanding QA staff capabilities to adapt to new changes in the manufacturing sector. ISO 22000:2018's requirement for competence and awareness (Clause 7.2 and 7.3) provides an existing framework within which AI literacy can and should be situated.

 

Industry-Regulator Collaboration on Validation Standards

The FAO report identifies three core areas of AI deployment in food safety: a) scientific advice, b) inspection and border control, and c) operational activities of food safety competent authorities. For each of these domains, validation standards analogous to method validation requirements for laboratory testing need to be developed. An AI system making predictions about import violation probability should be evaluated against documented accuracy metrics, tested across diverse shipment types and origins, and revalidated when the model or its training data changes. These validation requirements do not yet exist in a standardised form, and developing them is a concrete, achievable step that the food safety community can take in the near term.

 

The Unanswered Questions

It is important to be honest about what we do not yet know, because the honest answer to several critical questions is that nobody knows yet, and that uncertainty itself should inform how cautiously we proceed.

 

We do not know, at a population level, how frequently AI-generated food safety advice is wrong, or how frequently those errors have health consequences. The surveillance infrastructure to detect AI-related food safety misinformation at a population level simply does not exist. We do not know what the threshold of public trust in AI food safety advice is, at what point a pattern of notable errors would cause consumers to discount AI guidance in ways that might themselves create safety risks (for instance, by causing people to distrust correct AI advice about a genuine food safety hazard). We do not know whether the regulatory frameworks being developed by the EU, FDA, and others will evolve quickly enough to address the rate at which AI capabilities and deployments are changing.

 

The 2025 FAO and Wageningen report notes that the risk of prematurely using AI in food safety, whether by applying techniques that are not yet suitable for the specific data or problem, or by implementing AI without the necessary expertise to interpret its output, lies in potentially undermining the trust and credibility of the organisation employing it, which is well-stated. But it frames the risk at the organisational level. The deeper risk, as AI-generated food safety advice reaches consumers directly through voice assistants and AI summary features in search engines, is that it undermines public trust in food safety guidance more broadly, including the authoritative guidance that comes from regulatory bodies and certified professionals who have earned that trust over decades.

 

Conclusion

Artificial intelligence is not going to stop being applied to food safety, and nor should it. The genuine benefits, in pathogen detection speed, improve surveillance efficiency, supply chain traceability, and predictive risk modelling are real, documented, and important. The food safety community's task is not to resist AI adoption, but to shape it to demand well-designed systems, appropriate governance, embedded human expertise, and honest communication about the limits of what AI can reliably know.

 

The analogy that feels most apt is the introduction of rapid microbiological testing methods into food safety laboratories in the 1990s and 2000s. Those methods were faster, cheaper, and more scalable than traditional culture methods, and they required new validation standards, new competency requirements, and new quality assurance frameworks before they could be trusted. The industry built those frameworks, and rapid methods are now a cornerstone of modern food safety. AI can follow a similar trajectory, but only if the governance work keeps pace with the technology deployment, and in the current context, it is not.

 

Food safety professionals should be curious about AI, and should use it where it has been validated, verified its outputs against primary sources, and should invest in the AI literacy of their food safety teams. The regulators should realize that the window for proactive governance is narrowing, while consumers should treat AI food safety advice as a starting point for a question, not an endpoint for a decision. Thus, the question is not whether to trust AI in food safety, but rather how to build the conditions under which that trust is justified.

 

 

References

[1] van Meer, F., van der Velden, B. & Takeuchi, M. 2025. Artificial Intelligence for Food Safety – A Literature Synthesis, Real-World Applications and Regulatory Frameworks. FAO & Wageningen Food Safety Research. https://www.fao.org/food-safety/news/news-details/en/c/1748997/

[2] New Food Magazine. (2025, July 30). AI in food safety: real-world solutions from IAFP 2025. https://www.newfoodmagazine.com/news/253921/ai-in-food-safety-iafp-2025/

[3] International AI Safety Report 2026. International Scientific Report on the Safety of Advanced AI. https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026

[4] Food Safety Magazine. (2025, October 31). FAO Report Highlights Needs for Responsible AI Adoption in Food Safety Fields. https://www.food-safety.com/articles/10845-fao-report-highlights-needs-for-responsible-ai-adoption-in-food-safety-fields

[5] ScienceDirect. (2025, April). Advancing food safety behavior with AI: Innovations and opportunities in the food manufacturing sector. https://www.sciencedirect.com/science/article/pii/S0924224425001864

[6] Academia.edu / Open Access. (2025). Artificial intelligence in food safety and nutrition practices: opportunities and risks. https://www.academia.edu/3067-1345/2/3/10.20935/AcadNutr7904

[7] ScienceDirect. (2025, September). Food safety – the transition to artificial intelligence (AI) modus operandi. https://www.sciencedirect.com/science/article/pii/S0924224425004145

[8] ScienceDirect. (2025, September). Harnessing Artificial Intelligence to Safeguard Food Quality and Safety. https://www.sciencedirect.com/science/article/pii/S0362028X25001735

[9] Food Safety Magazine. (2026, March). Leveraging AI for Food Safety Without Becoming Its Victim. https://www.food-safety.com/articles/11280-leveraging-ai-for-food-safety-without-becoming-its-victim

[10] Exploration Publishing. (2025, October). The role of artificial intelligence (AI) in foodborne disease prevention and management—a mini literature review. https://www.explorationpub.com/Journals/edht/Article/101167